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The human gut virome: Inter-individual variation and dynamic response to diet
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Immense populations of viruses are present in the human gut and other body sites. Understanding the role of these populations (the human "virome") in health and disease requires a much deeper understanding of their composition and dynamics in the face of environmental perturbation. Here, we investigate viromes from human subjects on a controlled feeding regimen. Longitudinal fecal samples were analyzed by metagenomic sequencing of DNA from virus-like particles (VLP) and total microbial communities. Assembly of 336 Mb of VLP sequence yielded 7175 contigs, many identifiable as complete or partial bacteriophage genomes. Contigs were rich in viral functions required in lytic and lysogenic growth, as well as unexpected functions such as viral CRISPR arrays and genes for antibiotic resistance. The largest source of variance among virome samples was interpersonal variation. Parallel deep-sequencing analysis of bacterial populations showed covaration of the virome with the larger microbiome. The dietary intervention was associated with a change in the virome community to a new state, in which individuals on the same diet converged. Thus these data provide an overview of the composition of the human gut virome and associate virome structure with diet.
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“Diet has also been associated with other types of microbes in the gut, such as archaea, fungi, and bacteriophage (Figure 2)45-47.”
From Diet and the Intestinal Microbiome: Associations, Functions, and Implications for Health and Disease (2014) · cites this paper as [47]
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Metadata
- Authors
- Minot, S., Sinha, R., Chen, J., Li, H., Keilbaugh, S. A., Wu, G. D., Lewis, J. D., & Bushman, F. D.
- Affiliations
- University of Pennsylvania (US)
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2011-08-31
- Volume
- 21
- Issue
- 10
- Pages
- 1616-1625
- ISSN
- 1088-9051 (ISSN-L)
- PMC
- PMC3202279
- OpenAlex
- W2097146199
- Cited by
- 935
- References
- 58
- Citations by year
- 2026: 28 · 2025: 37 · 2024: 57 · 2023: 71 · 2022: 52 · 2021: 85 · 2020: 71 · 2019: 92 · 2018: 78 · 2017: 84 · 2016: 68 · 2015: 63 · 2014: 59 · 2013: 54 · 2012: 33
- Type
- posted content
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genome Research (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- Crohn's and Colitis Foundation of America
- Pennsylvania Department of Health
- University of Pennsylvania (UL1 RR024134)
- Crohn's and Colitis Foundation
- Georgia Clinical and Translational Science Alliance
- National Institutes of Health (DK050306)
- National Institute of Allergy and Infectious Diseases (R01AI039368, AI39368)
- National Institute of Diabetes and Digestive and Kidney Diseases (UH2DK083981, P30DK050306)
- National Center for Research Resources (UL1RR024134, S10RR024525)
- Topics
- Life Sciences › Immunology and Microbiology › Virology › Bacteriophages and microbial interactions
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Health Sciences › Medicine › Infectious Diseases › Viral gastroenteritis research and epidemiology
- Keywords
- gut virome, bacteriophages, virus-like particles, diet, dietary intervention, interindividual variability, gut microbiota, antibiotic resistance genes, viral metagenomics, viral community dynamics
- MeSH terms
- Adolescent, Adult, Bacteria, Bacteriophages, Feces, Genes, Bacterial, Genes, Viral, Humans, Repetitive Sequences, Nucleic Acid, RNA, Ribosomal, 16S, Virion, Open Reading Frames, Sequence Analysis, DNA, Diet, Fat-Restricted, Contig Mapping, Drug Resistance, Bacterial, Gastrointestinal Tract, Metagenome, Young Adult, High-Throughput Nucleotide Sequencing, Diet, High-Fat
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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